Awesome

DecryptPrompt

如果LLM的突然到来让你感到沮丧，不妨读下主目录的Choose Your Weapon Survival Strategies for Depressed AI Academics 持续更新以下内容，Star to keep updated~

LLM资源汇总

跟着博客读论文

论文汇总

paper List

综述

A Survey of Large Language Models
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing :star:
Paradigm Shift in Natural Language Processing
Pre-Trained Models: Past, Present and Future
What Language Model Architecture and Pretraining objects work best for zero shot generalization :star:
Towards Reasoning in Large Language Models: A Survey
Reasoning with Language Model Prompting: A Survey :star:
An Overview on Language Models: Recent Developments and Outlook :star:
A Survey of Large Language Models[6.29更新版]
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Augmented Language Models: a Survey :star:
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Challenges and Applications of Large Language Models
The Rise and Potential of Large Language Model Based Agents: A Survey
Large Language Models for Information Retrieval: A Survey
AI Alignment: A Comprehensive Survey
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
A Survey on Language Models for Code
Model-as-a-Service (MaaS): A Survey

大模型能力探究

In Context Learning
- LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY
- How does in-context learning work? A framework for understanding the differences from traditional supervised learning
- Why can GPT learn in-context? Language Model Secretly Perform Gradient Descent as Meta-Optimizers :star:
- Rethinking the Role of Demonstrations What Makes incontext learning work? :star:
- Trained Transformers Learn Linear Models In-Context
- In-Context Learning Creates Task Vectors
- FUNCTION VECTORS IN LARGE LANGUAGE MODELS
涌现能力
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- Emerging Ability of Large Language Models :star:
- LANGUAGE MODELS REPRESENT SPACE AND TIME
- Are Emergent Abilities of Large Language Models a Mirage?
能力评估
- IS CHATGPT A GENERAL-PURPOSE NATURAL LANGUAGE PROCESSING TASK SOLVER?
- Can Large Language Models Infer Causation from Correlation?
- Holistic Evaluation of Language Model
- Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
- Theory of Mind May Have Spontaneously Emerged in Large Language Models
- Beyond The Imitation Game: Quantifying And Extrapolating The Capabilities Of Language Models
- Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
- Demystifying GPT Self-Repair for Code Generation
- Evidence of Meaning in Language Models Trained on Programs
- Can Explanations Be Useful for Calibrating Black Box Models
- On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
- Language acquisition: do children and language models follow similar learning stages?
- Language is primarily a tool for communication rather than thought
领域能力
- Capabilities of GPT-4 on Medical Challenge Problems
- Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Prompt Tunning范式

Tunning Free Prompt
- GPT2: Language Models are Unsupervised Multitask Learners
- GPT3: Language Models are Few-Shot Learners :star:
- LAMA: Language Models as Knowledge Bases?
- AutoPrompt: Eliciting Knowledge from Language Models
Fix-Prompt LM Tunning
- T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- PET-TC(a): Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference :star:
- PET-TC(b): PETSGLUE It’s Not Just Size That Matters Small Language Models are also few-shot learners
- GenPET: Few-Shot Text Generation with Natural Language Instructions
- LM-BFF: Making Pre-trained Language Models Better Few-shot Learners :star:
- ADEPT: Improving and Simplifying Pattern Exploiting Training
Fix-LM Prompt Tunning
- Prefix-tuning: Optimizing continuous prompts for generation
- Prompt-tunning: The power of scale for parameter-efficient prompt tuning :star:
- P-tunning: GPT Understands Too :star:
- WARP: Word-level Adversarial ReProgramming
LM + Prompt Tunning
- P-tunning v2: Prompt Tuning Can Be Comparable to Fine-tunning Universally Across Scales and Tasks
- PTR: Prompt Tuning with Rules for Text Classification
- PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
Fix-LM Adapter Tunning
- LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS :star:
- LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
- Parameter-Efficient Transfer Learning for NLP
- INTRINSIC DIMENSIONALITY EXPLAINS THE EFFECTIVENESS OF LANGUAGE MODEL FINE-TUNING
- DoRA: Weight-Decomposed Low-Rank Adaptation
Representation Tuning
ReFT: Representation Finetuning for Language Models

主流LLMS和预训练

GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL
PaLM: Scaling Language Modeling with Pathways
PaLM 2 Technical Report
GPT-4 Technical Report
Backpack Language Models
LLaMA: Open and Efficient Foundation Language Models
Llama 2: Open Foundation and Fine-Tuned Chat Models
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
Mistral 7B
Ziya2: Data-centric Learning is All LLMs Need
MEGABLOCKS: EFFICIENT SPARSE TRAINING WITH MIXTURE-OF-EXPERTS
TUTEL: ADAPTIVE MIXTURE-OF-EXPERTS AT SCALE
Phi1- Textbooks Are All You Need :star:
Phi1.5- Textbooks Are All You Need II: phi-1.5 technical report
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Gemini: A Family of Highly Capable Multimodal Models
In-Context Pretraining: Language Modeling Beyond Document Boundaries
LLAMA PRO: Progressive LLaMA with Block Expansion
QWEN TECHNICAL REPORT
Fewer Truncations Improve Language Modeling
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

指令微调&对齐 (instruction_tunning)

经典方案
- Flan: FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS :star:
- Flan-T5: Scaling Instruction-Finetuned Language Models
- ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
- Instruct-GPT: Training language models to follow instructions with human feedback :star:
- T0: MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION
- Natural Instructions: Cross-Task Generalization via Natural Language Crowdsourcing Instructions
- Tk-INSTRUCT: SUPER-NATURALINSTRUCTIONS: Generalization via Declarative Instructions on 1600+ NLP Tasks
- ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-shot Generalization
- Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
- INSTRUCTEVAL Towards Holistic Evaluation of Instrucion-Tuned Large Language Models
SFT数据Scaling Law
- LIMA: Less Is More for Alignment :star:
- Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
- AlpaGasus: Training A Better Alpaca with Fewer Data
- InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
- Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
- Visual Instruction Tuning with Polite Flamingo
- Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
- Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
- WHEN SCALING MEETS LLM FINETUNING: THE EFFECT OF DATA, MODEL AND FINETUNING METHOD
新对齐/微调方案
- WizardLM: Empowering Large Language Models to Follow Complex Instructions :star:
- Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
- Self-Alignment with Instruction Backtranslation :star:
- Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
- Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
- PROMPT2MODEL: Generating Deployable Models from Natural Language Instructions
- OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
- Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
- Human-like systematic generalization through a meta-learning neural network
- Magicoder: Source Code Is All You Need
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
- Generative Representational Instruction Tuning
- InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
- The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
- Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
指令数据生成
- APE: LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS :star:
- SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions :star:
- iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting
- Flipped Learning: Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
- Fairness-guided Few-shot Prompting for Large Language Models
- Instruction induction: From few examples to natural language task descriptions .
- SELF-QA Unsupervised Knowledge Guided alignment.
- GPT Self-Supervision for a Better Data Annotator
- The Flan Collection Designing Data and Methods
- Self-Consuming Generative Models Go MAD
- InstructEval: Systematic Evaluation of Instruction Selection Methods
- Overwriting Pretrained Bias with Finetuning Data
- Improving Text Embeddings with Large Language Models
- MAGPIE: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
- Scaling Synthetic Data Creation with 1,000,000,000 Personas
如何降低通用能力损失
- How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
- TWO-STAGE LLM FINE-TUNING WITH LESS SPECIALIZATION AND MORE GENERALIZATION
微调经验/实验报告
- BELLE: Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
- Baize: Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
- A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Large LM
- Exploring ChatGPT’s Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences
- Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
- Fine tuning LLMs for Enterprise: Practical Guidelines and Recommendations
Others
- Crosslingual Generalization through Multitask Finetuning
- Cross-Task Generalization via Natural Language Crowdsourcing Instructions
- UNIFIEDSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
- PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
- ROLELLM: BENCHMARKING, ELICITING, AND ENHANCING ROLE-PLAYING ABILITIES OF LARGE LANGUAGE MODELS

对话模型

LaMDA: Language Models for Dialog Applications
Sparrow: Improving alignment of dialogue agents via targeted human judgements :star:
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
DiagGPT: An LLM-based Chatbot with Automatic Topic Management for Task-Oriented Dialogue

思维链 (prompt_chain_of_thought)

基础&进阶用法
- 【zero-shot-COT】 Large Language Models are Zero-Shot Reasoners :star:
- 【few-shot COT】 Chain of Thought Prompting Elicits Reasoning in Large Language Models :star:
- 【SELF-CONSISTENCY 】IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
- 【LEAST-TO-MOST】 PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS :star:
- 【TOT】Tree of Thoughts: Deliberate Problem Solving with Large Language Models :star:
- 【Plan-and-Solve】 Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
- 【Verify-and-Edit】: A Knowledge-Enhanced Chain-of-Thought Framework
- 【GOT】Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models
- 【TOMT】Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
- 【LAMBADA】: Backward Chaining for Automated Reasoning in Natural Language
- 【AOT】Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models :star:
- 【GOT】Graph of Thoughts: Solving Elaborate Problems with Large Language Models :star:
- 【PHP】Progressive-Hint Prompting Improves Reasoning in Large Language Models
- 【HtT】LARGE LANGUAGE MODELS CAN LEARN RULES :star:
- 【DIVSE】DIVERSITY OF THOUGHT IMPROVES REASONING ABILITIES OF LARGE LANGUAGE MODELS
- 【CogTree】From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
- 【Step-Back】Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models :star:
- 【OPRO】LARGE LANGUAGE MODELS AS OPTIMIZERS :star:
- 【BOT】Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
- Abstraction-of-Thought Makes Language Models Better Reasoners
- 【SymbCoT】Faithful Logical Reasoning via Symbolic Chain-of-Thought
- 【XOT】EVERYTHING OF THOUGHTS : DEFYING THE LAW OF PENROSE TRIANGLE FOR THOUGHT GENERATION
- 【IoT】Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
- 【DOT】On the Diagram of Thought
非传统COT问题分解方向
- Decomposed Prompting A MODULAR APPROACH FOR Solving Complex Tasks
- Successive Prompting for Decomposing Complex Questions
分领域COT [Math, Code, Tabular, QA]
- Solving Quantitative Reasoning Problems with Language Models
- SHOW YOUR WORK: SCRATCHPADS FOR INTERMEDIATE COMPUTATION WITH LANGUAGE MODELS
- Solving math word problems with processand outcome-based feedback
- CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
- T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering
- LEARNING PERFORMANCE-IMPROVING CODE EDITS
- Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
原理分析
- Chain of Thought Empowers Transformers to Solve Inherently Serial Problems :star:
- Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters :star:
- TEXT AND PATTERNS: FOR EFFECTIVE CHAIN OF THOUGHT IT TAKES TWO TO TANGO
- Towards Revealing the Mystery behind Chain of Thought: a Theoretical Perspective
- Large Language Models Can Be Easily Distracted by Irrelevant Context
- Chain-of-Thought Reasoning Without Prompting
- Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
- Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
- To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning :star:
- Why think step by step? Reasoning emerges from the locality of experience
小模型COT蒸馏
- Specializing Smaller Language Models towards Multi-Step Reasoning :star:
- Teaching Small Language Models to Reason
- Large Language Models are Reasoning Teachers
- Distilling Reasoning Capabilities into Smaller Language Models
- The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
- Distilling System 2 into System 1
COT样本自动构建/选择
- AutoCOT：AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS
- Active Prompting with Chain-of-Thought for Large Language Models
- COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING
COT能力学习
- Large Language Models Can Self-Improve
- Training Chain-of-Thought via Latent-Variable Inference
- Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
- STaR: Self-Taught Reasoner Bootstrapping ReasoningWith Reasoning
- V-STaR: Training Verifiers for Self-Taught Reasoners
- THINK BEFORE YOU SPEAK: TRAINING LANGUAGE MODELS WITH PAUSE TOKENS
- SELF-DIRECTED SYNTHETIC DIALOGUES AND REVISIONS TECHNICAL REPORT
others
- OlaGPT Empowering LLMs With Human-like Problem-Solving abilities
- Challenging BIG-Bench tasks and whether chain-of-thought can solve them
- Large Language Models are Better Reasoners with Self-Verification
- ThoughtSource A central hub for large language model reasoning data
- Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

RLHF

Deepmind
- Teaching language models to support answers with verified quotes
- sparrow, Improving alignment of dialogue agents via targetd human judgements :star:
- STATISTICAL REJECTION SAMPLING IMPROVES PREFERENCE OPTIMIZATION
- Reinforced Self-Training (ReST) for Language Modeling
- SLiC-HF: Sequence Likelihood Calibration with Human Feedback
- CALIBRATING SEQUENCE LIKELIHOOD IMPROVES CONDITIONAL LANGUAGE GENERATION
- REWARD DESIGN WITH LANGUAGE MODELS
- Final-Answer RL Solving math word problems with processand outcome-based feedback
- Solving math word problems with process- and outcome-based feedback
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
- BOND: Aligning LLMs with Best-of-N Distillation
- RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
- Generative Verifiers: Reward Modeling as Next-Token Prediction
- Training Language Models to Self-Correct via Reinforcement Learning
openai
- PPO: Proximal Policy Optimization Algorithms :star:
- Deep Reinforcement Learning for Human Preference
- Fine-Tuning Language Models from Human Preferences
- learning to summarize from human feedback
- InstructGPT: Training language models to follow instructions with human feedback :star:
- Scaling Laws for Reward Model Over optimization :star:
- WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION :star:
- PRM：Let's verify step by step :star:
- Training Verifiers to Solve Math Word Problems [PRM的前置依赖]
- OpenAI Super Alignment Blog
- LLM Critics Help Catch LLM Bugs :star:
- PROVER-VERIFIER GAMES IMPROVE LEGIBILITY OF LLM OUTPUTS
- Rule Based Rewards for Language Model Safety
- Self-critiquing models for assisting human evaluators
Anthropic
- A General Language Assistant as a Laboratory for Alignmen
- Red Teaming Language Models to Reduce Harms Methods,Scaling Behaviors and Lessons Learned
- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback :star:
- Constitutional AI Harmlessness from AI Feedback :star:
- Pretraining Language Models with Human Preferences
- The Capacity for Moral Self-Correction in Large Language Models
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainin
AllenAI, RL4LM：IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING BENCHMARKS
改良方案
- RRHF: Rank Responses to Align Language Models with Human Feedback without tears
- Chain of Hindsight Aligns Language Models with Feedback
- AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
- RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
- RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
- Training Socially Aligned Language Models in Simulated Human Society
- RAIN: Your Language Models Can Align Themselves without Finetuning
- Generative Judge for Evaluating Alignment
- PEERING THROUGH PREFERENCES: UNRAVELING FEEDBACK ACQUISITION FOR ALIGNING LARGE LANGUAGE MODELS
- SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS
- Large Language Model Unlearning :star:
- ADVERSARIAL PREFERENCE OPTIMIZATION :star:
- Preference Ranking Optimization for Human Alignment
- A Long Way to Go: Investigating Length Correlations in RLHF
- ENABLE LANGUAGE MODELS TO IMPLICITLY LEARN SELF-IMPROVEMENT FROM DATA
- REWARD MODEL ENSEMBLES HELP MITIGATE OVEROPTIMIZATION
- LEARNING OPTIMAL ADVANTAGE FROM PREFERENCES AND MISTAKING IT FOR REWARD
- ULTRAFEEDBACK: BOOSTING LANGUAGE MODELS WITH HIGH-QUALITY FEEDBACK
- MOTIF: INTRINSIC MOTIVATION FROM ARTIFICIAL INTELLIGENCE FEEDBACK
- STABILIZING RLHF THROUGH ADVANTAGE MODEL AND SELECTIVE REHEARSAL
- Shepherd: A Critic for Language Model Generation
- LEARNING TO GENERATE BETTER THAN YOUR LLM
- Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
- Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model
- HIR The Wisdom of Hindsight Makes Language Models Better Instruction Followers
- Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction
- A Minimaximalist Approach to Reinforcement Learning from Human Feedback
- PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
- Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
- Weak-to-Strong Extrapolation Expedites Alignment
- Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
- Token-level Direct Preference Optimization
- SimPO: Simple Preference Optimization with a Reference-Free Reward
- AUTODETECT: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
- META-REWARDING LANGUAGE MODELS: Self-Improving Alignment with LLM-as-a-Meta-Judge
- HELPSTEER: Multi-attribute Helpfulness Dataset for STEERLM
- Recursive Introspection: Teaching Language Model Agents How to Self-Improve
- Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
RL探究
- UNDERSTANDING THE EFFECTS OF RLHF ON LLM GENERALISATION AND DIVERSITY
- A LONG WAY TO GO: INVESTIGATING LENGTH CORRELATIONS IN RLHF
- THE TRICKLE-DOWN IMPACT OF REWARD (IN-)CONSISTENCY ON RLHF
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
- HUMAN FEEDBACK IS NOT GOLD STANDARD
- CONTRASTIVE POST-TRAINING LARGE LANGUAGE MODELS ON DATA CURRICULUM
- Language Models Resist Alignment
Inference Scaling
- An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
- Are More LM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters :star:
- Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
- Planning In Natural Language Improves LLM Search For Code Generation
- ReST-MCTS∗ : LLM Self-Training via Process Reward Guided Tree Search
- AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
- Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

LLM Agent 让模型使用工具 (llm_agent)

A Survey on Large Language Model based Autonomous Agents
PERSONAL LLM AGENTS: INSIGHTS AND SURVEY ABOUT THE CAPABILITY, EFFICIENCY AND SECURITY
基于prompt通用方案
- ReAct: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS :star:
- Self-ask: MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS :star:
- MRKL SystemsA modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
- PAL: Program-aided Language Models
- ART: Automatic multi-step reasoning and tool-use for large language models
- ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models :star:
- Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
- Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models :star:
- Faithful Chain-of-Thought Reasoning
- Reflexion: Language Agents with Verbal Reinforcement Learning :star:
- Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
- RestGPT: Connecting Large Language Models with Real-World RESTful APIs
- ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
- InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
- TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents
- ControlLLM: Augment Language Models with Tools by Searching on Graphs
- Reflexion: an autonomous agent with dynamic memory and self-reflection
- AutoAgents: A Framework for Automatic Agent Generation
- GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
- PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
- TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS :star: -AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
- AIOS: LLM Agent Operating System
- LLMCompiler An LLM Compiler for Parallel Function Calling
- Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval
基于微调通用方案
- TALM: Tool Augmented Language Models
- Toolformer: Language Models Can Teach Themselves to Use Tools :star:
- Tool Learning with Foundation Models
- Tool Maker：Large Language Models as Tool Maker
- TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
- AgentTuning: Enabling Generalized Agent Abilities for LLMs
- SWIFTSAGE: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
- FireAct: Toward Language Agent Fine-tuning
- Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
- REST MEETS REACT: SELF-IMPROVEMENT FOR MULTI-STEP REASONING LLM AGENT
- Efficient Tool Use with Chain-of-Abstraction Reasoning
- Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
- AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
- Agent Lumos: Unified and Modular Training for Open-Source Language Agents
- ToolGen: Unified Tool Retrieval and Calling via Generation
调用模型方案
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
- Gorilla：Large Language Model Connected with Massive APIs :star:
- OpenAGI: When LLM Meets Domain Experts
垂直领域
- 数据分析
  - DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
  - InsightLens: Discovering and Exploring Insights from Conversational Contexts in Large-Language-Model-Powered Data Analysis
  - Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
  - Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
  - TaskWeaver: A Code-First Agent Framework
  - Automated Social Science: Language Models as Scientist and Subjects
  - Data Interpreter: An LLM Agent For Data Science
- 金融
  - WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
  - FinGPT: Open-Source Financial Large Language Models
  - FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
  - AlphaFin：使用检索增强股票链框架对财务分析进行基准测试
  - A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist :star:
  - Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in stock Selection
  - ENHANCING ANOMALY DETECTION IN FINANCIAL MARKETS WITH AN LLM-BASED MULTI-AGENT FRAMEWORK
  - TRADINGGPT: MULTI-AGENT SYSTEM WITH LAYERED MEMORY AND DISTINCT CHARACTERS FOR ENHANCED FINANCIAL TRADING PERFORMANCE
  - FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
  - LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
  - Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
  - Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
- 生物医疗
  - GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
  - ChemCrow Augmenting large language models with chemistry tools
  - Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
  - Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
  - Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
- web/mobile Agent
  - AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
  - A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
  - Mind2Web: Towards a Generalist Agent for the Web
  - MiniWoB++ Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
  - WEBARENA: A REALISTIC WEB ENVIRONMENT FORBUILDING AUTONOMOUS AGENTS
  - AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
  - WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
  - WebVoyager: Building an End-to-end Web Agent with Large Multimodal Models
  - CogAgent: A Visual Language Model for GUI Agents
  - Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
  - WebCanvas: Benchmarking Web Agents in Online Environments
- software engineer
- Agents in Software Engineering: Survey, Landscape, and Vision
- ChatDev: Communicative Agents for Software Development
- 其他
  - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
  - WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
  - ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
  - PointLLM: Empowering Large Language Models to Understand Point Clouds
  - Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
  - CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
  - SCIAGENTS: AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING
评估
- Evaluating Verifiability in Generative Search Engines
- Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions
- API-Bank: A Benchmark for Tool-Augmented LLMs
- ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
- Automatic Evaluation of Attribution by Large Language Models
- Benchmarking Large Language Models in Retrieval-Augmented Generation
- ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
MultiAgent
- GENERATIVE AGENTS
- LET MODELS SPEAK CIPHERS: MULTIAGENT DEBATE THROUGH EMBEDDINGS
- War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
- Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
- Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
- Generative Agents: Interactive Simulacra of Human Behavior :star:
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
- System-1.x: Learning to Balance Fast and Slow Planning with Language Models
- 多智能体系统
  - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
  - MULTI-AGENT COLLABORATION: HARNESSING THE POWER OF INTELLIGENT LLM AGENTS
- 任务型智能体协作
  - METAAGENTS: SIMULATING INTERACTIONS OF HUMAN BEHAVIORS FOR LLM-BASED TASK-ORIENTED COORDINATION VIA COLLABORATIVE
  - CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society :star:
  - Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
  - Communicative Agents for Software Development :star:
  - MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
  - METAGPT: META PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK
- 智能体路由
  - One Agent To Rule Them All: Towards Multi-agent Conversational AI
  - A Multi-Agent Conversational Recommender System
- 基座模型路由&Ensemble
  - Large Language Model Routing with Benchmark Datasets
  - LLM-BL E N D E R: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
  - RouteLLM: Learning to Route LLMs with Preference Data
  - More Agents Is All You Need
  - Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
自主学习和探索进化
- AppAgent: Multimodal Agents as Smartphone Users
- Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
- LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
- Empowering Large Language Model Agents through Action Learning
- Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
- OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT
- LLAMA RIDER: SPURRING LARGE LANGUAGE MODELS TO EXPLORE THE OPEN WORLD
- PAST AS A GUIDE: LEVERAGING RETROSPECTIVE LEARNING FOR PYTHON CODE COMPLETION
- AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
- A Survey on Self-Evolution of Large Language Models
- ExpeL: LLM Agents Are Experiential Learners
- ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
其他
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- Inference with Reference: Lossless Acceleration of Large Language Models
- RecallM: An Architecture for Temporal Context Understanding and Question Answering
- LLaMA Rider: Spurring Large Language Models to Explore the Open World
- LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

RAG

经典论文
- WebGPT：Browser-assisted question-answering with human feedback
- WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
- WebCPM: Interactive Web Search for Chinese Long-form Question Answering :star:
- REPLUG: Retrieval-Augmented Black-Box Language Models :star:
- RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
- Atlas: Few-shot Learning with Retrieval Augmented Language Models
- RRAML: Reinforced Retrieval Augmented Machine Learning
- FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION
微调
- RLCF：Aligning the Capabilities of Large Language Models with the Context of Information Retrieval via Contrastive Feedback
- RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING
- CHAIN-OF-NOTE: ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS
- RAFT: Adapting Language Model to Domain Specific RAG
- Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
其他论文
- Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
- PDFTriage: Question Answering over Long, Structured Documents
- Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading :star:
- Active Retrieval Augmented Generation
- kNN-LM Does Not Improve Open-ended Text Generation
- Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
- DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
- Factuality Enhanced Language Models for Open-Ended Text Generation
- KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
- Complex Claim Verification with Evidence Retrieved in the Wild
- Retrieval-Augmented Generation for Large Language Models: A Survey
- ChatQA: Building GPT-4 Level Conversational QA Models
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
- Benchmarking Large Language Models in Retrieval-Augmented Generation
- T-RAG: Lessons from the LLM Trenches
- ARAGOG: Advanced RAG Output Grading
- ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
- OpenResearcher: Unleashing AI for Accelerated Scientific Research
- Contextual.ai-RAG2.0
- Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation
- Memory3 : Language Modeling with Explicit Memory
优化检索
- IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
- HyDE：Precise Zero-Shot Dense Retrieval without Relevance Labels
- PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES
- Query Rewriting for Retrieval-Augmented Large Language Models
- Query2doc: Query Expansion with Large Language Models :star:
- Query Expansion by Prompting Large Language Models :star:
- Anthropic Contextual Retrieval
- Multi-Level Querying using A Knowledge Pyramid
Ranking
- A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
- RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
- Improving Passage Retrieval with Zero-Shot Question Generation
- Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
- RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
- Ranking Manipulation for Conversational Search Engines
- Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
- Opensource Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
- T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
- Learning to Filter Context for Retrieval-Augmented Generation
传统搜索方案
- ASK THE RIGHT QUESTIONS:ACTIVE QUESTION REFORMULATION WITH REINFORCEMENT LEARNING
- Query Expansion Techniques for Information Retrieval a Survey
- Learning to Rewrite Queries
- Managing Diversity in Airbnb Search
新向量模型用于Recall和Ranking
- Augmented Embeddings for Custom Retrievals
- BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
- 网易为RAG设计的BCE Embedding技术报告
- BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models
- D2LLM: Decomposed and Distilled Large Language Models for Semantic Search
- Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
优化推理结果
- Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
动态RAG（When to Search & Search Plan）
- SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION :star:
- Self-Knowledge Guided Retrieval Augmentation for Large Language Models
- Self-DC: When to retrieve and When to generate Self Divide-and-Conquer for Compositional Unknown Questions
- Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
- Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
- REAPER: Reasoning based Retrieval Planning for Complex RAG Systems
- When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
- PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers
- ONEGEN: EFFICIENT ONE-PASS UNIFIED GENERATION AND RETRIEVAL FOR LLMS
Graph RAG
- GRAPH Retrieval-Augmented Generation: A Survey
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- GRAG: Graph Retrieval-Augmented Generation
- GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
- THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASONING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH
- LightRAG: Simple and Fast Retrieval-Augmented Generation
- THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASON- ING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH
Multistep RAG
- SYNERGISTIC INTERPLAY BETWEEN SEARCH AND LARGE LANGUAGE MODELS FOR INFORMATION RETRIEVAL
- Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
- Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
- RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
- IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues
- Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
- Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks
- MindSearch 思·索: Mimicking Human Minds Elicits Deep AI Searcher

大模型图表理解和生成

survey
- Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
- Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey
- Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
prompt
- Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
- Tab-CoT: Zero-shot Tabular Chain of Thought
- Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
fintuning
- TableLlama: Towards Open Large Generalist Models for Tables
- TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
multimodal
- MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
- ChartLlama: A Multimodal LLM for Chart Understanding and Generation
- ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
- ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
- ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
- MATCHA : Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
- UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
- TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
- Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
- TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
- TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy

LLM+KG

综述类
- Unifying Large Language Models and Knowledge Graphs: A Roadmap
- Large Language Models and Knowledge Graphs: Opportunities and Challenges
- 知识图谱与大模型融合实践研究报告2023
KG用于大模型推理
- Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs
- MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
- Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
- Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models
- BRING YOUR OWN KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
- StructGPT: A General Framework for Large Language Model to Reason over Structured Data
大模型用于KG构建
- Enhancing Knowledge Graph Construction Using Large Language Models
- LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
- ITERATIVE ZERO-SHOT LLM PROMPTING FOR KNOWLEDGE GRAPH CONSTRUCTION
- Exploring Large Language Models for Knowledge Graph Completion

Humanoid Agents

HABITAT 3.0: A CO-HABITAT FOR HUMANS, AVATARS AND ROBOTS
Humanoid Agents: Platform for Simulating Human-like Generative Agents
Voyager: An Open-Ended Embodied Agent with Large Language Models
Shaping the future of advanced robotics
AUTORT: EMBODIED FOUNDATION MODELS FOR LARGE SCALE ORCHESTRATION OF ROBOTIC AGENTS
ROBOTIC TASK GENERALIZATION VIA HINDSIGHT TRAJECTORY SKETCHES
ALFWORLD: ALIGNING TEXT AND EMBODIED ENVIRONMENTS FOR INTERACTIVE LEARNING
MINEDOJO: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
LEGENT: Open Platform for Embodied Agents

pretrain_data & pretrain

DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
CCNet: Extracting High Quality Monolingual Datasets fromWeb Crawl Data
WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Zyda: A 1.3T Dataset for Open Language Modeling
Entropy Law: The Story Behind Data Compression and LLM Performance
Data, Data Everywhere: A Guide for Pretraining Dataset Construction
Data curation via joint example selection further accelerates multimodal learning
IMPROVING PRETRAINING DATA USING PERPLEXITY CORRELATIONS
AI models collapse when trained on recursively generated data

领域模型SFT(domain_llms)

金融
- BloombergGPT： A Large Language Model for Finance
- FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
- CFGPT: Chinese Financial Assistant with Large Language Model
- CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
- InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
- BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
- PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
- The FinBen: An Holistic Financial Benchmark for Large Language Models
- XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
- Towards Trustworthy Large Language Models in Industry Domains
- When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
- A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges
生物医疗
- MedGPT: Medical Concept Prediction from Clinical Narratives
- BioGPT：Generative Pre-trained Transformer for Biomedical Text Generation and Mining
- PubMed GPT: A Domain-specific large language model for biomedical text :star:
- ChatDoctor：Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
- Med-PaLM：Large Language Models Encode Clinical Knowledge[V1,V2] :star:
- SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
- Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
其他
- Galactia：A Large Language Model for Science
- Augmented Large Language Models with Parametric Knowledge Guiding
- ChatLaw Open-Source Legal Large Language Model :star:
- MediaGPT : A Large Language Model For Chinese Media
- KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
- EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
- TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
- LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS
- MEDITAB: SCALING MEDICAL TABULAR DATA PREDICTORS VIA DATA CONSOLIDATION, ENRICHMENT, AND REFINEMENT
- PLLaMa: An Open-source Large Language Model for Plant Science
- ADAPTING LARGE LANGUAGE MODELS VIA READING COMPREHENSION

LLM超长文本处理 (long_input)

位置编码、注意力机制优化
- Unlimiformer: Long-Range Transformers with Unlimited Length Input
- Parallel Context Windows for Large Language Models
- 苏剑林, NBCE：使用朴素贝叶斯扩展LLM的Context处理长度 :star:
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples
- Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
- Scaling Transformer to 1M tokens and beyond with RMT
- TRAIN SHORT, TEST LONG: ATTENTION WITH LINEAR BIASES ENABLES INPUT LENGTH EXTRAPOLATION :star:
- Extending Context Window of Large Language Models via Positional Interpolation
- LongNet: Scaling Transformers to 1,000,000,000 Tokens
- https://kaiokendev.github.io/til#extending-context-to-8k
- 苏剑林,Transformer升级之路：10、RoPE是一种β进制编码 :star:
- 苏剑林,Transformer升级之路：11、将β进制位置进行到底
- 苏剑林,Transformer升级之路：12、无限外推的ReRoPE？
- 苏剑林,Transformer升级之路：15、Key归一化助力长度外推
- EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS
- Ring Attention with Blockwise Transformers for Near-Infinite Context
- YaRN: Efficient Context Window Extension of Large Language Models
- LM-INFINITE: SIMPLE ON-THE-FLY LENGTH GENERALIZATION FOR LARGE LANGUAGE MODELS
- EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS
上文压缩排序方案
- Lost in the Middle: How Language Models Use Long Contexts :star:
- LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
- LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression :star:
- Learning to Compress Prompts with Gist Tokens
- Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering
- LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
- PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models
- Are Long-LLMs A Necessity For Long-Context Tasks?
训练和模型架构方案
- Never Train from Scratch: FAIR COMPARISON OF LONGSEQUENCE MODELS REQUIRES DATA-DRIVEN PRIORS
- Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
- Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering
- Focused Transformer: Contrastive Training for Context Scaling
- Effective Long-Context Scaling of Foundation Models
- ON THE LONG RANGE ABILITIES OF TRANSFORMERS
- Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
- POSE: EFFICIENT CONTEXT WINDOW EXTENSION OF LLMS VIA POSITIONAL SKIP-WISE TRAINING
- LONGLORA: EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS
- LongAlign: A Recipe for Long Context Alignment of Large Language Models
- Data Engineering for Scaling Language Models to 128K Context
- MEGALODON: Efficient LLM Pretraining and Inference with Unlimited Context Length
- Make Your LLM Fully Utilize the Context
- Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models
效率优化
- Efficient Attention: Attention with Linear Complexities
- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
- HyperAttention: Long-context Attention in Near-Linear Time
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation

LLM长文本生成（long_output）

Re3 : Generating Longer Stories With Recursive Reprompting and Revision
RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text
DOC: Improving Long Story Coherence With Detailed Outline Control
Weaver: Foundation Models for Creative Writing
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

NL2SQL

大模型方案
- DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction :star:
- C3: Zero-shot Text-to-SQL with ChatGPT :star:
- SQL-PALM: IMPROVED LARGE LANGUAGE MODEL ADAPTATION FOR TEXT-TO-SQL
- BIRD Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQL :star:
- A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL
- ChatDB: AUGMENTING LLMS WITH DATABASES AS THEIR SYMBOLIC MEMORY
- A comprehensive evaluation of ChatGPT’s zero-shot Text-to-SQL capability
- Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning
- Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios
Domain Knowledge Intensive
- Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
- Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion
- Towards Robustness of Text-to-SQL Models against Synonym Substitution
- FinQA: A Dataset of Numerical Reasoning over Financial Data
others
- RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
- MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL

Code Generation

Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Codeforces as an Educational Platform for Learning Programming in Digitalization
Competition-Level Code Generation with AlphaCode
CODECHAIN: TOWARDS MODULAR CODE GENERATION THROUGH CHAIN OF SELF-REVISIONS WITH REPRESENTATIVE SUB-MODULES
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation

降低模型幻觉 (reliability)

Survey
- Large language models and the perils of their hallucinations
- Survey of Hallucination in Natural Language Generation
- Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
- A Survey of Hallucination in Large Foundation Models
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- Calibrated Language Models Must Hallucinate
- Why Does ChatGPT Fall Short in Providing Truthful Answers?
Prompt or Tunning
- R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
- PROMPTING GPT-3 TO BE RELIABLE
- ASK ME ANYTHING: A SIMPLE STRATEGY FOR PROMPTING LANGUAGE MODELS :star:
- On the Advance of Making Language Models Better Reasoners
- RefGPT: Reference → Truthful & Customized Dialogues Generation by GPTs and for GPTs
- Rethinking with Retrieval: Faithful Large Language Model Inference
- GENERATE RATHER THAN RETRIEVE: LARGE LANGUAGE MODELS ARE STRONG CONTEXT GENERATORS
- Large Language Models Struggle to Learn Long-Tail Knowledge
Decoding Strategy
- Trusting Your Evidence: Hallucinate Less with Context-aware Decoding :star:
- SELF-REFINE:ITERATIVE REFINEMENT WITH SELF-FEEDBACK :star:
- Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
- Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
- Enabling Large Language Models to Generate Text with Citations
- Factuality Enhanced Language Models for Open-Ended Text Generation
- KL-Divergence Guided Temperature Sampling
- KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
- CONTRASTIVE DECODING IMPROVES REASONING IN LARGE LANGUAGE MODEL
- Contrastive Decoding: Open-ended Text Generation as Optimization
Probing and Detection
- Automatic Evaluation of Attribution by Large Language Models
- QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
- Zero-Resource Hallucination Prevention for Large Language Models
- LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
- Language Models (Mostly) Know What They Know :star:
- LM vs LM: Detecting Factual Errors via Cross Examination
- Do Language Models Know When They’re Hallucinating References?
- SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
- SELF-CONTRADICTORY HALLUCINATIONS OF LLMS: EVALUATION, DETECTION AND MITIGATION
- Self-consistency for open-ended generations
- Improving Factuality and Reasoning in Language Models through Multiagent Debate
- Selective-LAMA: Selective Prediction for Confidence-Aware Evaluation of Language Models
- Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
Reviewing and Calibration
- Truth-o-meter: Collaborating with llm in fighting its hallucinations
- RARR: Researching and Revising What Language Models Say, Using Language Models
- CRITIC: LARGE LANGUAGE MODELS CAN SELFCORRECT WITH TOOL-INTERACTIVE CRITIQUING
- VALIDATING LARGE LANGUAGE MODELS WITH RELM
- PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
- Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
- Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes
- Woodpecker: Hallucination Correction for Multimodal Large Language Models
- Zero-shot Faithful Factual Error Correction

大模型评估（evaluation）

事实性评估
- TRUSTWORTHY LLMS: A SURVEY AND GUIDELINE FOR EVALUATING LARGE LANGUAGE MODELS’ ALIGNMENT
- TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
- TRUE: Re-evaluating Factual Consistency Evaluation
- FACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
- KoLA: Carefully Benchmarking World Knowledge of Large Language Models
- When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
- FACTOOL: Factuality Detection in Generative AI A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
- LONG-FORM FACTUALITY IN LARGE LANGUAGE MODELS
检测任务
- Detecting Pretraining Data from Large Language Models
- Scalable Extraction of Training Data from (Production) Language Models
- Rethinking Benchmark and Contamination for Language Models with Rephrased Samples

推理优化(inference)

Fast Transformer Decoding: One Write-Head is All You Need
Fast Inference from Transformers via Speculative Decoding
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
BatchPrompt: Accomplish more with less
You Only Cache Once: Decoder-Decoder Architectures for Language Models

模型知识编辑黑科技(model_edit)

ROME：Locating and Editing Factual Associations in GPT
Transformer Feed-Forward Layers Are Key-Value Memories
MEMIT: Mass-Editing Memory in a Transformer
MEND：Fast Model Editing at Scale
Editing Large Language Models: Problems, Methods, and Opportunities
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Automata-based constraints for language model decoding
SGLang: Efficient Execution of Structured Language Model Programs
PROMPT CACHE: MODULAR ATTENTION REUSE FOR LOW-LATENCY INFERENCE

模型合并和剪枝(model_merge)

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
DARE Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
EDITING MODELS WITH TASK ARITHMETIC
TIES-Merging: Resolving Interference When Merging Models
LM-Cocktail: Resilient Tuning of Language Models via Model Merging
SLICEGPT: COMPRESS LARGE LANGUAGE MODELS BY DELETING ROWS AND COLUMNS
Checkpoint Merging via Bayesian Optimization in LLM Pretrainin
Arcee's MergeKit: A Toolkit for Merging Large Language Models

MOE

Tricks for Training Sparse Translation Models
ST-MoE: Designing Stable and Transferable Sparse Expert Models
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
Dense-to-Sparse Gate for Mixture-of-Experts
Efficient Large Scale Language Modeling with Mixtures of Experts

Other Prompt Engineer(prompt_engineer)

Calibrate Before Use: Improving Few-Shot Performance of Language Models
In-Context Instruction Learning
LEARNING PERFORMANCE-IMPROVING CODE EDITS
Boosting Theory-of-Mind Performance in Large Language Models via Prompting
Generated Knowledge Prompting for Commonsense Reasoning
RECITATION-AUGMENTED LANGUAGE MODELS
kNN PROMPTING: BEYOND-CONTEXT LEARNING WITH CALIBRATION-FREE NEAREST NEIGHBOR INFERENCE
EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus
Causality-aware Concept Extraction based on Knowledge-guided Prompting
LARGE LANGUAGE MODELS AS OPTIMIZERS
Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
MedPrompt: Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
In-Context Learning for Extreme Multi-Label Classification
Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
CONNECTING LARGE LANGUAGE MODELS WITH EVOLUTIONARY ALGORITHMS YIELDS POWERFUL PROMP OPTIMIZERS
TextGrad: Automatic "Differentiation" via Text
Task Facet Learning: A Structured Approach to Prompt Optimization
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

Multimodal

InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
LLava Visual Instruction Tuning
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
mPLUG-Owl : Modularization Empowers Large Language Models with Multimodality
LVLM eHub: A Comprehensive Evaluation Benchmark for Large VisionLanguage Models
Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
PaLM-E: An Embodied Multimodal Language Model
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
Sora tech report
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
OCR
- Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
- Large OCR Model:An Empirical Study of Scaling Law for OCR
- ON THE HIDDEN MYSTERY OF OCR IN LARGE MULTIMODAL MODELS
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Many-Shot In-Context Learning in Multimodal Foundation Models
Adding Conditional Control to Text-to-Image Diffusion Models

Timeseries LLM

TimeGPT-1
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
TIME-LLM: TIME SERIES FORECASTING BY REPROGRAMMING LARGE LANGUAGE MODELS
Large Language Models Are Zero-Shot Time Series Forecasters
TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING
Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing
Lag-Llama: Towards Foundation Models for Time Series Forecasting
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

Quanization

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
LLM.int8() 8-bit Matrix Multiplication for Transformers at Scale
SmoothQuant Accurate and Efficient Post-Training Quantization for Large Language Models

Adversarial Attacking

Curiosity-driven Red-teaming for Large Language Models
Red Teaming Language Models with Language Models
EXPLORE, ESTABLISH, EXPLOIT: RED-TEAMING LANGUAGE MODELS FROM SCRATCH

Others

Pretraining on the Test Set Is All You Need 哈哈作者你是懂讽刺文学的
Learnware: Small Models Do Big
The economic potential of generative AI
A PhD Student’s Perspective on Research in NLP in the Era of Very Large Language Models